Deep learning for brain metastasis detection and segmentation in longitudinal MRI data

Brain metastases occur frequently in patients with metastatic cancer. Early and accurate detection of brain metastases is very essential for treatment planning and prognosis in radiation therapy. To improve brain metastasis detection performance with deep learning, a custom detection loss called volume-level sensitivity-specificity (VSS) is proposed, which rates individual metastasis detection sensitivity and specificity in (sub-)volume levels. As sensitivity and precision are always a trade-off in a metastasis level, either a high sensitivity or a high precision can be achieved by adjusting the weights in the VSS loss without decline in dice score coefficient for segmented metastases. To reduce metastasis-like structures being detected as false positive metastases, a temporal prior volume is proposed as an additional input of DeepMedic. The modified network is called DeepMedic+ for distinction. Our proposed VSS loss improves the sensitivity of brain metastasis detection for DeepMedic, increasing the sensitivity from 85.3% to 97.5%. Alternatively, it improves the precision from 69.1% to 98.7%. Comparing DeepMedic+ with DeepMedic with the same VSS loss, 44.4% of the false positive metastases are reduced in the high sensitivity model and the precision reaches 99.6% for the high specificity model. The mean dice coefficient for all metastases is about 0.81. With the ensemble of the high sensitivity and high specificity models, on average only 1.5 false positive metastases per patient needs further check, while the majority of true positive metastases are confirmed. The ensemble learning is able to distinguish high confidence true positive metastases from metastases candidates that require special expert review or further follow-up, being particularly well-fit to the requirements of expert support in real clinical practice.Comment: Implementation is available to public at https://github.com/YixingHuang/DeepMedicPlu

Multiparametric Imaging and MR Image Texture Analysis in Brain Tumors

Discrimination of tumor from radiation injured (RI) tissues and differentiation of tumor types using noninvasive imaging is essential for guiding surgical and radiotherapy treatments are some of the challenges that clinicians face in the course of treatment of brain tumors. The first objective in this thesis was to develop a method to discriminate between glioblastoma tumor recurrences and radiation injury using multiparametric characterization of the tissue incorporating conventional magnetic resonance imaging signal intensities and diffusion tensor imaging parameters. Our results show significant correlations in the RI that was missing in the tumor regions. These correlations may aid in differentiating between tumor recurrence and RI. The second objective of was to investigate whether texture based image analysis of routine MR images would provide quantitative information that could be used to differentiate between glioblastoma and metastasis. Our results demonstrate that first-order texture feature of standard deviation and second-order texture features of entropy, inertia, homogeneity, and energy show significant differences between the two groups. The third objective was to investigate whether quantitative measurements of tumor size and appearance on MRI scans acquired prior to helical tomotherapy (HT) type whole brain radiotherapy with simultaneous infield boost treatment could be used to differentiate responder and non-responder patient groups. Our results demonstrated that smaller size lesions may respond better to this type of radiation therapy. Measures of appearance provided limited added value over measures of size for response prediction. Quantitative measurements of rim enhancement and core necrosis performed separately did not provide additional predictive value

Texture Analysis Platform for Imaging Biomarker Research

abstract: The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope. Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers. This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201

Studies on Category Prediction of Ovarian Cancers Based on Magnetic Resonance Images

Ovarian cancer is the gynecological malignant tumor with low early diagnosis rate and high mortality. Ovarian epithelial cancer (OEC) is the most common subtype of ovarian cancer. Pathologically, OEC is divided into two subtypes: Type I and Type II. These two subtypes of OEC have different biological characteristics and treatment response. Therefore, it is important to accurately categorize these two groups of patients and provide the reference for clinicians in designing treatment plans. In the current magnetic resonance (MR) examination, the diagnoses given by the radiologists are largely based on individual judgment and not sufficiently accurate. Because of the low accuracy of the results and the risk of suffering Type II OEC, most patients will undertake the fine-needle aspiration, which may cause harm to patients’ bodies. Therefore, there is need for the method for OEC subtype classification based on MR images. This thesis proposes the automatic diagnosis system of ovarian cancer based on the combination of deep learning and radiomics. The method utilizes four common useful sequences for ovarian cancer diagnosis: sagittal fat-suppressed T2WI (Sag-fs-T2WI), coronal T2WI (Cor-T2WI), axial T1WI (Axi-T1WI), and apparent diffusion coefficient map (ADC) to establish a multi-sequence diagnostic model. The system starts with the segmentation of the ovarian tumors, and then obtains the radiomic features from lesion parts together with the network features. Selected Features are used to build model to predict the malignancy of ovarian cancers, the subtype of OEC and the survival condition. Bi-atten-ResUnet is proposed in this thesis as the segmentation model. The network is established on the basis of U-Net with adopting Residual block and non-local attention module. It preserves the classic encoder/decoder architecture in the U-Net network. The encoder part is reconstructed by the pretrained ResNet to make use of transfer learning knowledge, and bi-non-local attention modules are added to the decoder part on each level. The application of these techniques enhances the network’s performance in segmentation tasks. The model achieves 0.918, 0.905, 0.831, and 0.820 Dice coefficient respectively in segmenting on four MR sequences. After the segmentation work, the thesis proposes a diagnostic model with three steps: quantitative description feature extraction, feature selection, and establishment of prediction models. First, radiomic features and network features are obtained. Then iterative sparse representation (ISR) method is adopted as the feature selection to reduce the redundancy and correlation. The selected features are used to establish a predictive model, and support vector machine (SVM) is used as the classifier. The model achieves an AUC of 0.967 in distinguishing between benign and malignant ovarian tumors. For discriminating Type I and Type II OEC, the model yields an AUC of 0.823. In the survival prediction, patients categorized in high risk group are more likely to have poor prognosis with hazard ratio 4.169

Improving the Clinical Use of Magnetic Resonance Spectroscopy for the Analysis of Brain Tumours using Machine Learning and Novel Post-Processing Methods

Magnetic Resonance Spectroscopy (MRS) provides unique and clinically relevant information for the assessment of several diseases. However, using the currently available tools, MRS processing and analysis is time-consuming and requires profound expert knowledge. For these two reasons, MRS did not gain general acceptance as a mainstream diagnostic technique yet, and the currently available clinical tools have seen little progress during the past years. MRS provides localized chemical information non-invasively, making it a valuable technique for the assessment of various diseases and conditions, namely brain, prostate and breast cancer, and metabolic diseases affecting the brain. In brain cancer, MRS is normally used for: (1.) differentiation between tumors and non-cancerous lesions, (2.) tumor typing and grading, (3.) differentiation between tumor-progression and radiation necrosis, and (4.) identification of tumor infiltration. Despite the value of MRS for these tasks, susceptibility differences associated with tissue-bone and tissue-air interfaces, as well as with the presence of post-operative paramagnetic particles, affect the quality of brain MR spectra and consequently reduce their clinical value. Therefore, the proper quality management of MRS acquisition and processing is essential to achieve unambiguous and reproducible results. In this thesis, special emphasis was placed on this topic. This thesis addresses some of the major problems that limit the use of MRS in brain tumors and focuses on the use of machine learning for the automation of the MRS processing pipeline and for assisting the interpretation of MRS data. Three main topics were investigated: (1.) automatic quality control of MRS data, (2.) identification of spectroscopic patterns characteristic of different tissue-types in brain tumors, and (3.) development of a new approach for the detection of tumor-related changes in GBM using MRSI data. The first topic tackles the problem of MR spectra being frequently affected by signal artifacts that obscure their clinical information content. Manual identification of these artifacts is subjective and is only practically feasible for single-voxel acquisitions and in case the user has an extensive experience with MRS. Therefore, the automatic distinction between data of good or bad quality is an essential step for the automation of MRS processing and routine reporting. The second topic addresses the difficulties that arise while interpreting MRS results: the interpretation requires expert knowledge, which is not available at every site. Consequently, the development of methods that enable the easy comparison of new spectra with known spectroscopic patterns is of utmost importance for clinical applications of MRS. The third and last topic focuses on the use of MRSI information for the detection of tumor-related effects in the periphery of brain tumors. Several research groups have shown that MRSI information enables the detection of tumor infiltration in regions where structural MRI appears normal. However, many of the approaches described in the literature make use of only a very limited amount of the total information contained in each MR spectrum. Thus, a better way to exploit MRSI information should enable an improvement in the detection of tumor borders, and consequently improve the treatment of brain tumor patients. The development of the methods described was made possible by a novel software tool for the combined processing of MRS and MRI: SpectrIm. This tool, which is currently distributed as part of the jMRUI software suite (www.jmrui.eu), is ubiquitous to all of the different methods presented and was one of the main outputs of the doctoral work. Overall, this thesis presents different methods that, when combined, enable the full automation of MRS processing and assist the analysis of MRS data in brain tumors. By allowing clinical users to obtain more information from MRS with less effort, this thesis contributes to the transformation of MRS into an important clinical tool that may be available whenever its information is of relevance for patient management

PET/MRI 및 MR-IGRT를 위한 MRI 기반 합성 CT 생성의 타당성 연구

학위논문 (박사) -- 서울대학교 대학원 : 의과대학 의과학과, 2020. 8. 이재성.Over the past decade, the application of magnetic resonance imaging (MRI) in the field of diagnosis and treatment has increased. MRI provides higher soft-tissue contrast, especially in the brain, abdominal organ, and bone marrow without the expose of ionizing radiation. Hence, simultaneous positron emission tomography/MR (PET/MR) system and MR-image guided radiation therapy (MR-IGRT) system has recently been emerged and currently available for clinical study. One major issue in PET/MR system is attenuation correction from MRI scans for PET quantification and a similar need for the assignment of electron densities to MRI scans for dose calculation can be found in MR-IGRT system. Because the MR signals are related to the proton density and relaxation properties of tissue, not to electron density. To overcome this problem, the method called synthetic CT (sCT), a pseudo CT derived from MR images, has been proposed. In this thesis, studies on generating synthetic CT and investigating the feasibility of using a MR-based synthetic CT for diagnostic and radiotherapy application were presented. Firstly, MR image-based attenuation correction (MR-AC) method using level-set segmentation for brain PET/MRI was developed. To resolve conventional inaccuracy MR-AC problem, we proposed an improved ultrashort echo time MR-AC method that was based on a multiphase level-set algorithm with main magnetic field inhomogeneity correction. We also assessed the feasibility of level-set based MR-AC method, compared with CT-AC and MR-AC provided by the manufacturer of the PET/MRI scanner. Secondly, we proposed sCT generation from the low field MR images using 2D convolution neural network model for MR-IGRT system. This sCT images were compared to the deformed CT generated using the deformable registration being used in the current system. We assessed the feasibility of using sCT for radiation treatment planning from each of the patients with pelvic, thoraic and abdominal region through geometric and dosimetric evaluation.지난 10년간 진단 및 치료분야에서 자기공명영상(Magnetic resonance imaging; MRI) 의 적용이 증가하였다. MRI는 CT와 비교해 추가적인 전리방사선의 피폭없이 뇌, 복부 기관 및 골수 등에서 더 높은 연조직 대비를 제공한다. 따라서 MRI를 적용한 양전자방출단층촬영(Positron emission tomography; PET)/MR 시스템과 MR 영상 유도 방사선 치료 시스템(MR-image guided radiation therapy; MR-IGRT)이 진단 및 치료 방사선분야에 등장하여 임상에 사용되고 있다. PET/MR 시스템의 한 가지 주요 문제는 PET 정량화를 위한 MRI 스캔으로부터의 감쇠 보정이며, MR-IGRT 시스템에서 선량 계산을 위해 MR 영상에 전자 밀도를 할당하는 것과 비슷한 필요성을 찾을 수 있다. 이는 MR 신호가 전자 밀도가 아닌 조직의 양성자 밀도 및 T1, T2 이완 특성과 관련이 있기 때문이다. 이 문제를 극복하기 위해, MR 이미지로부터 유래된 가상의 CT인 합성 CT라 불리는 방법이 제안되었다. 본 학위논문에서는 합성 CT 생성 방법 및 진단 및 방사선 치료에 적용을 위한 MR 영상 기반 합성 CT 사용의 임상적 타당성을 조사하였다. 첫째로, 뇌 PET/MR를 위한 레벨셋 분할을 이용한 MR 이미지 기반 감쇠 보정 방법을 개발하였다. MR 이미지 기반 감쇠 보정의 부정확성은 정량화 오류와 뇌 PET/MRI 연구에서 병변의 잘못된 판독으로 이어진다. 이 문제를 해결하기 위해, 자기장 불균일 보정을 포함한 다상 레벨셋 알고리즘에 기초한 개선된 초단파 에코 시간 MR-AC 방법을 제안하였다. 또한 CT-AC 및 PET/MRI 스캐너 제조업체가 제공한 MR-AC와 비교하여 레벨셋 기반 MR-AC 방법의 임상적 사용가능성을 평가하였다. 둘째로, MR-IGRT 시스템을 위한 심층 컨볼루션 신경망 모델을 사용하여 저필드 MR 이미지에서 생성된 합성 CT 방법를 제안하였다. 이 합성 CT 이미지를 변형 정합을 사용하여 생성된 변형 CT와 비교 하였다. 또한 골반, 흉부 및 복부 환자에서의 기하학적, 선량적 분석을 통해 방사선 치료계획에서의 합성 CT를 사용가능성을 평가하였다.Chapter 1. Introduction 1 1.1. Background 1 1.1.1. The Integration of MRI into Other Medical Devices 1 1.1.2. Chanllenges in the MRI Integrated System 4 1.1.3. Synthetic CT Generation 5 1.2. Purpose of Research 6 Chapter 2. MRI-based Attenuation Correction for PET/MRI 8 2.1. Background 8 2.2. Materials and Methods 10 2.2.1. Brain PET Dataset 19 2.2.2. MR-Based Attenuation Map using Level-Set Algorithm 12 2.2.3. Image Processing and Reconstruction 18 2.3. Results 20 2.4. Discussion 28 Chapter 3. MRI-based synthetic CT generation for MR-IGRT 30 3.1. Background 30 3.2. Materials and Methods 32 3.2.1. MR-dCT Paired DataSet 32 3.2.2. Synthetic CT Generation using 2D CNN 36 3.2.3. Data Analysis 38 3.3. Results 41 3.3.1. Image Comparison 41 3.3.2. Geometric Analysis 49 3.3.3. Dosimetric Analysis 49 3.4. Discussion 56 Chapter 4. Conclusions 59 Bibliography 60 Abstract in Korean (국문 초록) 64Docto

Multimodal Ultrasound Imaging for Improved Metastatic Lymph Node Detection

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide and is complex in nature due to the variety of organs located in the head and neck region. Knowing the metastatic state of the lymph nodes is paramount in accurately staging and treating HNSCC patients. Currently, metastatic lymph node detection involves the use of magnetic resonance imaging and/or x-ray computed tomography, followed by biopsies for histological confirmation. The main diagnostic criteria is the size of the nodes; however, current imaging methods are not 100% accurate due natural lymph node variability. Ultrasound imaging is able to provide additional biological information in addition to lymph node size such as the hilus state, presence of necrosis and vascular information, but it is hindered by poor resolution and limited contrast. Augmenting ultrasound for metastatic lymph node detection has clinical potential due to the availability of ultrasound in the clinic, reduced radiation exposure and minimized patient morbidity. This thesis focuses on augmenting ultrasound with photoacoustic imaging or with nanoparticle contrast agents for improved detection of lymph node metastasis. First, the development of an ultrasound-photoacoustic (USPA) imaging system is described. The USPA system is capable of imaging blood oxygen saturation (sO2), a promising criteria to differentiate between metastatic and healthy lymph nodes. To correct for tissue-dependent attenuation of light in tissue, a deep neural network was developed and trained using Monte-Carlo simulated and experimentally acquired photoacoustic data for better sO2 predictions. Secondly, to improve ultrasound sensitivity to metastatic cells, molecularly targeted phase change perfluorohexane nanodroplets conjugated to epidermal growth factor receptor (EGFR) antibodies (PFHnD-Abs) were developed. It is shown that the PFHnD-Abs are able to specifically bind to HNSCC cells and improve the ultrasound contrast of the cells, opening the door to targeted metastatic lymph node detection. Lastly, to validate the use of the PFHnD-Abs in-vivo, a paired agent imaging approach was adopted by using using a perfluoropentane core nanodroplet (PFPnD) as a non-targeted imaging agent to enable multiplex ultrasound imaging in vivo. Overall, this work expands the potential of ultrasound for metastatic lymph node detection